code: def format_metrics(metrics, split):
      """Formats metrics for logging.
  
      Args:
        metrics: Dictionary with metrics.
        split: String indicating the KG dataset split.
  
      Returns:
        String with formatted metrics.
      """
      result = "\t {} MR: {:.2f} | ".format(split, metrics["MR"])
      result += "MRR: {:.3f} | ".format(metrics["MRR"])
      result += "H@1: {:.3f} | ".format(metrics["hits@[1,3,10]"][0])
      result += "H@3: {:.3f} | ".format(metrics["hits@[1,3,10]"][1])
      result += "H@10: {:.3f}".format(metrics["hits@[1,3,10]"][2])
      return result
  
file_path: neural-structured-learning/research/kg_hyp_emb/utils/train.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl import flags
  from kg_hyp_emb.config import CONFIG
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: format_metrics(metrics, split) and returns: result